This paper describes an algorithm for detecting empty nodes in the Penn Treebank (Marcus et al., 1993), finding their antecedents, and assigning them function tags, without access to lexical information such as valency. Unlike previous approaches to this task, the current method is not corpus-based, but rather makes use of the principles of early Government-Binding theory (Chomsky, 1981), the syntactic theory that underlies the annotation. Using the evaluation metric proposed by Johnson (2002), this approach outperforms previously published approaches on both detection of empty categories and antecedent identification, given either annotated input stripped of empty categories or the output of a parser. Some problems with this evaluation metric are noted and an alternative is proposed along with the results. The paper considers the reasons a principle-based approach to this problem should outperform corpus-based approaches, and speculates on the possibility of a hybrid approach.

Intelligent rooms equipped with video cameras can exhibit compelling behaviors, many of which depend on object recognition. Unfortunately, object recognition algorithms are rarely written with a normal consumer in mind, leading to programs that would be impractical to use for a typical person. These impracticalities include speed of execution, elaborate training rituals, and setting adjustable parameters. We present an algorithm that can be trained with only a few images of the object, that requires only two parameters to be set, and that runs at 0.7 Hz on a normal PC with a normal color camera. The algorithm represents an object’s features as small, quantized edge templates, and it represents the object’s geometry with “Hough kernels”.